Artificial neural networks (ANN) represent a family of statistical learning models, which derive from our biological neural networks. An ANN is represented by an interconnected group of nodes, which is similar in structure and function to the neurons in a brain. ANNs communicate among layers and attempt to minimize statistical error through training. The connections have numeric weights that can be adapted to minimize classification errors. ANNs are best suited to applications that are difficult, if not impossible, to realize using rule-based systems—including machine vision and automated speech understanding.
Much recent effort has focused on development of scalable intelligent systems that mimic the working of the human brain. Contemporary neural networks have been used for such intelligent systems, as provide one or more hidden layers for generalizing learning. However, this feature renders the learning algorithm intractable and thus not reusable. It follows that not only do these neural networks not function as does the brain, but they cannot even emulate brain function. There exists a need for improved intelligent systems and methods that can better emulate the functioning of the human brain, for applications such as image recognition.